Prior study of Data and Databases and Core Skills

1. CRITICALLY EVALUATE APPROPRIATE METHODOLOGIES FOR SOLVING QUANTITATIVE PROBLEMS. Analysis
Problem Solving

2. RESEARCH AND EVALUATE ISSUES WHICH AFFECT ENTERPRISE DATA.
Enquiry
Knowledge & understanding
Reflection
Analysis

3. IMPLEMENT BUSINESS REPORTING SOLUTIONS IN THE CONTEXT OF ENTERPRISE DATA.
Application

4. CRITICALLY EVALUATE APPROACHES IN DATA MANAGEMENT AND COMMUNICATE YOUR RESULTS. Problem solving
Communication

You will need access to these resources:

Blackboard (VLE)
Microsoft Excel
Microsoft Power BI
Microsoft Azure Portal
Pycharm IDE, Python extension, libraries for Data mining and data visualisation (e.g., Numpy, Panda, Plotly, Matplotlib)
LinkedIn Learning
Access to VM – Sandbox within the desktop environment
Latest browser (e.g., edge chromium, Google Chrome, or another up to date browser)

Module Launch (18 hours)

There will be a module launch during which 10 hours of face-to-face contact will be devoted to undertaking tasks which are designed to provide useful insights into the module content and purpose.

Guided Learning (20 hours)

A module tutor who is part of the teaching team of the module will be allocated to you and you will meet them during the launch. Following the launch, there will be some materials on the VLE which are designed to guide your learning. Additionally, there will be at least two-hour long sessions per week of contact time for the eleven weeks following the launch. This will be used for learning guided led by your module tutor. It will be a face-to-face presentation if you are on day release. For online learners it will be flipped classroom approach with group (up to 20) seminars.

Reviews:
Tutorial reviews for online learners (total of 2 hours)
Online learners will have 2 tutorial sessions with their module tutor during the course of the module. These will be individual or small group sessions during which your module tutor will be able to answer any queries that you have regarding module work. The review weeks are listed in the module handbook and mentors will be invited to join the call and provide feedback.

Tutorial sessions for day release learners (total of 2 hours)
There will be scheduled tutorial sessions (up to 20 students) during the 11 weeks following the launch which will take the place of the tutorial reviews for day release students.

Independent learning (260 hours)

The module leader will provide resources through the virtual learning environment which will include videos and presentations as well as links to useful websites. Other academic learning will be achieved through reading around the subject area. Module tutors will suggest useful texts, though many others will be suitable and can be found in our e-library. If you require help understanding any of the concepts, you may contact your module tutor for assistance.

Part of your independent learning will take place in your workplace under the guidance of your mentor. You will complete a work-based learning agreement to ensure that arrangements are in place at your workplace to facilitate this work-based learning. You are encouraged to endeavour to apply your growing academic knowledge to improve your work practice and to reflect on your work-based experiences to improve your learning.

You will be required to complete assignment work during independent learning time. Assignment work for a 30-credit module at level 5 should take around 120 hours to complete.

Additional help with learning
You will have access to the departmental librarian. As a student, you are more than welcome to visit the university at any time and to use the resources. During time at the university, you may arrange to meet your module tutor or academic coach for additional help.

Fundamentals and core data concepts:
Data characteristics
Data workloads
Batch and Streaming
Analytics

Fundamentals of database concepts in a cloud environment:
Relational data, non-relational data
SQL, MariaDB and MySQL, Cosmos DB

Modern data warehouse analytics:
Data collection and preparation
Model planning, building, selection, and evaluation
Data interpretation and visualization
Cloud data services within Microsoft Azure

Data analytics tools such as Excel and Power Query, and Microsoft Power BI

Assessment 1: weighted at 70% - A practical assignment, to design and implement an artefact, supported by a 1500-word report (Learning outcomes 1, 2, 3 and 4).

Assessment 2: weighted at 30% - A 15-minute presentation, describing the needs analysis, and justifying the choice made for the assignment (Learning outcomes 1, 2 and 4).

Harper, J. (2019) Data Science for Business: How to Use Data Analytics and Data Mining in Business, Big Data for Business, Springer, ISBN:9781386573241

Gressel, S, & Pauleen, D, J, (2020), Management Decision-Making, Big Data and Analytics, SAGE Publications Ltd; 1st edition, ISBN-10:¿1526492008

Gilbert, S, (2021), Good Data: An Optimist's Guide to Our Digital Future, Welbeck, ISBN-10:¿1787396363

Paul, D. (2014) Business analysis, 3rd edn, BCS, Swindon. ISBN: 178017277 x; 9781780172774.

Krishnan, K. (2013) Data Warehousing in the Age of Big Data. San Francisco: Elsevier Science & Technology. ISBN: 0124058914

Gordon, K. (2013) Principles of Data Management (2nd edition) ISBN: 9781780171845

This module introduces the fundamentals of data science and data analytics: an emergent specialised area of computer science that is concerned with knowledge on datamining and visualisation, including state of the art database platforms, development toolkits, and industrial application scenarios. This module will look at the usage of Cloud Data services within Microsoft Azure as a base for the practical work.

Students will be exposed to core data analytics concepts and models, the current technology landscape, and topical application scenarios using a variety of environments and open datasets.

For student studying this module, you will learn how to use the Microsoft Azure cloud data services, and you will also be studying towards your Microsoft Azure Data Fundamentals certification.